An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization

Abstract Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on t...
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Autor*in:	Gomes, Cláudio [verfasserIn] Falcao, Gabriel Paquete, Luís Fernandes, João Paulo

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Quantum annealing Portfolio optimization Quadratic unconstrained binary optimization Combinatorial optimization

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022

Übergeordnetes Werk:	Enthalten in: SN Computer Science - Singapore : Springer Singapore, 2020, 3(2022), 5 vom: 15. Juni
Übergeordnetes Werk:	volume:3 ; year:2022 ; number:5 ; day:15 ; month:06

Links:	Volltext

DOI / URN:	10.1007/s42979-022-01215-9

Katalog-ID:	SPR047294051

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allfields	10.1007/s42979-022-01215-9 doi (DE-627)SPR047294051 (SPR)s42979-022-01215-9-e DE-627 ger DE-627 rakwb eng Gomes, Cláudio verfasserin (orcid)0000-0001-6292-0222 aut An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022 Abstract Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on the practical use of QC to tackle a combinatorial optimization problem in Finance, the Portfolio Optimization Problem (POP). We present an in-depth empirical study on the influence that configurable parameters of both a state-of-the-art adiabatic quantum computer and POP itself can have on the overall quality of the solutions we obtain. Our results show that some of these parameters, such as chain strength and a number of reads, have a significant statistical effect, while others, such as anneal schedule and embedding, do not. Our results also show that the quality of the solutions returned by a quantum computer, given a quadratic unconstrained binary optimization formulation of POP from the literature, is still far from the quality of the solutions produced by a classical computer using an exact algorithm. We believe the conclusions drawn from our study are valuable contributions to the utilization of adiabatic quantum computers in practice, not only in the context of POP but also for other application domains. Quantum annealing (dpeaa)DE-He213 Portfolio optimization (dpeaa)DE-He213 Quadratic unconstrained binary optimization (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Falcao, Gabriel (orcid)0000-0001-9805-6747 aut Paquete, Luís (orcid)0000-0001-7525-8901 aut Fernandes, João Paulo (orcid)0000-0002-1952-9460 aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 3(2022), 5 vom: 15. Juni (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:3 year:2022 number:5 day:15 month:06 https://dx.doi.org/10.1007/s42979-022-01215-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2022 5 15 06
spelling	10.1007/s42979-022-01215-9 doi (DE-627)SPR047294051 (SPR)s42979-022-01215-9-e DE-627 ger DE-627 rakwb eng Gomes, Cláudio verfasserin (orcid)0000-0001-6292-0222 aut An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022 Abstract Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on the practical use of QC to tackle a combinatorial optimization problem in Finance, the Portfolio Optimization Problem (POP). We present an in-depth empirical study on the influence that configurable parameters of both a state-of-the-art adiabatic quantum computer and POP itself can have on the overall quality of the solutions we obtain. Our results show that some of these parameters, such as chain strength and a number of reads, have a significant statistical effect, while others, such as anneal schedule and embedding, do not. Our results also show that the quality of the solutions returned by a quantum computer, given a quadratic unconstrained binary optimization formulation of POP from the literature, is still far from the quality of the solutions produced by a classical computer using an exact algorithm. We believe the conclusions drawn from our study are valuable contributions to the utilization of adiabatic quantum computers in practice, not only in the context of POP but also for other application domains. Quantum annealing (dpeaa)DE-He213 Portfolio optimization (dpeaa)DE-He213 Quadratic unconstrained binary optimization (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Falcao, Gabriel (orcid)0000-0001-9805-6747 aut Paquete, Luís (orcid)0000-0001-7525-8901 aut Fernandes, João Paulo (orcid)0000-0002-1952-9460 aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 3(2022), 5 vom: 15. Juni (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:3 year:2022 number:5 day:15 month:06 https://dx.doi.org/10.1007/s42979-022-01215-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2022 5 15 06
allfields_unstemmed	10.1007/s42979-022-01215-9 doi (DE-627)SPR047294051 (SPR)s42979-022-01215-9-e DE-627 ger DE-627 rakwb eng Gomes, Cláudio verfasserin (orcid)0000-0001-6292-0222 aut An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022 Abstract Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on the practical use of QC to tackle a combinatorial optimization problem in Finance, the Portfolio Optimization Problem (POP). We present an in-depth empirical study on the influence that configurable parameters of both a state-of-the-art adiabatic quantum computer and POP itself can have on the overall quality of the solutions we obtain. Our results show that some of these parameters, such as chain strength and a number of reads, have a significant statistical effect, while others, such as anneal schedule and embedding, do not. Our results also show that the quality of the solutions returned by a quantum computer, given a quadratic unconstrained binary optimization formulation of POP from the literature, is still far from the quality of the solutions produced by a classical computer using an exact algorithm. We believe the conclusions drawn from our study are valuable contributions to the utilization of adiabatic quantum computers in practice, not only in the context of POP but also for other application domains. Quantum annealing (dpeaa)DE-He213 Portfolio optimization (dpeaa)DE-He213 Quadratic unconstrained binary optimization (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Falcao, Gabriel (orcid)0000-0001-9805-6747 aut Paquete, Luís (orcid)0000-0001-7525-8901 aut Fernandes, João Paulo (orcid)0000-0002-1952-9460 aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 3(2022), 5 vom: 15. Juni (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:3 year:2022 number:5 day:15 month:06 https://dx.doi.org/10.1007/s42979-022-01215-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2022 5 15 06
allfieldsGer	10.1007/s42979-022-01215-9 doi (DE-627)SPR047294051 (SPR)s42979-022-01215-9-e DE-627 ger DE-627 rakwb eng Gomes, Cláudio verfasserin (orcid)0000-0001-6292-0222 aut An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022 Abstract Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on the practical use of QC to tackle a combinatorial optimization problem in Finance, the Portfolio Optimization Problem (POP). We present an in-depth empirical study on the influence that configurable parameters of both a state-of-the-art adiabatic quantum computer and POP itself can have on the overall quality of the solutions we obtain. Our results show that some of these parameters, such as chain strength and a number of reads, have a significant statistical effect, while others, such as anneal schedule and embedding, do not. Our results also show that the quality of the solutions returned by a quantum computer, given a quadratic unconstrained binary optimization formulation of POP from the literature, is still far from the quality of the solutions produced by a classical computer using an exact algorithm. We believe the conclusions drawn from our study are valuable contributions to the utilization of adiabatic quantum computers in practice, not only in the context of POP but also for other application domains. Quantum annealing (dpeaa)DE-He213 Portfolio optimization (dpeaa)DE-He213 Quadratic unconstrained binary optimization (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Falcao, Gabriel (orcid)0000-0001-9805-6747 aut Paquete, Luís (orcid)0000-0001-7525-8901 aut Fernandes, João Paulo (orcid)0000-0002-1952-9460 aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 3(2022), 5 vom: 15. Juni (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:3 year:2022 number:5 day:15 month:06 https://dx.doi.org/10.1007/s42979-022-01215-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2022 5 15 06
allfieldsSound	10.1007/s42979-022-01215-9 doi (DE-627)SPR047294051 (SPR)s42979-022-01215-9-e DE-627 ger DE-627 rakwb eng Gomes, Cláudio verfasserin (orcid)0000-0001-6292-0222 aut An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022 Abstract Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on the practical use of QC to tackle a combinatorial optimization problem in Finance, the Portfolio Optimization Problem (POP). We present an in-depth empirical study on the influence that configurable parameters of both a state-of-the-art adiabatic quantum computer and POP itself can have on the overall quality of the solutions we obtain. Our results show that some of these parameters, such as chain strength and a number of reads, have a significant statistical effect, while others, such as anneal schedule and embedding, do not. Our results also show that the quality of the solutions returned by a quantum computer, given a quadratic unconstrained binary optimization formulation of POP from the literature, is still far from the quality of the solutions produced by a classical computer using an exact algorithm. We believe the conclusions drawn from our study are valuable contributions to the utilization of adiabatic quantum computers in practice, not only in the context of POP but also for other application domains. Quantum annealing (dpeaa)DE-He213 Portfolio optimization (dpeaa)DE-He213 Quadratic unconstrained binary optimization (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Falcao, Gabriel (orcid)0000-0001-9805-6747 aut Paquete, Luís (orcid)0000-0001-7525-8901 aut Fernandes, João Paulo (orcid)0000-0002-1952-9460 aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 3(2022), 5 vom: 15. Juni (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:3 year:2022 number:5 day:15 month:06 https://dx.doi.org/10.1007/s42979-022-01215-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2022 5 15 06
language	English
source	Enthalten in SN Computer Science 3(2022), 5 vom: 15. Juni volume:3 year:2022 number:5 day:15 month:06
sourceStr	Enthalten in SN Computer Science 3(2022), 5 vom: 15. Juni volume:3 year:2022 number:5 day:15 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Quantum annealing Portfolio optimization Quadratic unconstrained binary optimization Combinatorial optimization
isfreeaccess_bool	false
container_title	SN Computer Science
authorswithroles_txt_mv	Gomes, Cláudio @@aut@@ Falcao, Gabriel @@aut@@ Paquete, Luís @@aut@@ Fernandes, João Paulo @@aut@@
publishDateDaySort_date	2022-06-15T00:00:00Z
hierarchy_top_id	1668832976
id	SPR047294051
language_de	englisch
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author	Gomes, Cláudio
spellingShingle	Gomes, Cláudio misc Quantum annealing misc Portfolio optimization misc Quadratic unconstrained binary optimization misc Combinatorial optimization An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization
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topic_title	An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization Quantum annealing (dpeaa)DE-He213 Portfolio optimization (dpeaa)DE-He213 Quadratic unconstrained binary optimization (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213
topic	misc Quantum annealing misc Portfolio optimization misc Quadratic unconstrained binary optimization misc Combinatorial optimization
topic_unstemmed	misc Quantum annealing misc Portfolio optimization misc Quadratic unconstrained binary optimization misc Combinatorial optimization
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title	An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization
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title_full	An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization
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author_browse	Gomes, Cláudio Falcao, Gabriel Paquete, Luís Fernandes, João Paulo
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title_sort	empirical study on the use of quantum computing for financial portfolio optimization
title_auth	An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization
abstract	Abstract Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on the practical use of QC to tackle a combinatorial optimization problem in Finance, the Portfolio Optimization Problem (POP). We present an in-depth empirical study on the influence that configurable parameters of both a state-of-the-art adiabatic quantum computer and POP itself can have on the overall quality of the solutions we obtain. Our results show that some of these parameters, such as chain strength and a number of reads, have a significant statistical effect, while others, such as anneal schedule and embedding, do not. Our results also show that the quality of the solutions returned by a quantum computer, given a quadratic unconstrained binary optimization formulation of POP from the literature, is still far from the quality of the solutions produced by a classical computer using an exact algorithm. We believe the conclusions drawn from our study are valuable contributions to the utilization of adiabatic quantum computers in practice, not only in the context of POP but also for other application domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022
abstractGer	Abstract Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on the practical use of QC to tackle a combinatorial optimization problem in Finance, the Portfolio Optimization Problem (POP). We present an in-depth empirical study on the influence that configurable parameters of both a state-of-the-art adiabatic quantum computer and POP itself can have on the overall quality of the solutions we obtain. Our results show that some of these parameters, such as chain strength and a number of reads, have a significant statistical effect, while others, such as anneal schedule and embedding, do not. Our results also show that the quality of the solutions returned by a quantum computer, given a quadratic unconstrained binary optimization formulation of POP from the literature, is still far from the quality of the solutions produced by a classical computer using an exact algorithm. We believe the conclusions drawn from our study are valuable contributions to the utilization of adiabatic quantum computers in practice, not only in the context of POP but also for other application domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022
abstract_unstemmed	Abstract Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on the practical use of QC to tackle a combinatorial optimization problem in Finance, the Portfolio Optimization Problem (POP). We present an in-depth empirical study on the influence that configurable parameters of both a state-of-the-art adiabatic quantum computer and POP itself can have on the overall quality of the solutions we obtain. Our results show that some of these parameters, such as chain strength and a number of reads, have a significant statistical effect, while others, such as anneal schedule and embedding, do not. Our results also show that the quality of the solutions returned by a quantum computer, given a quadratic unconstrained binary optimization formulation of POP from the literature, is still far from the quality of the solutions produced by a classical computer using an exact algorithm. We believe the conclusions drawn from our study are valuable contributions to the utilization of adiabatic quantum computers in practice, not only in the context of POP but also for other application domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022
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title_short	An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization
url	https://dx.doi.org/10.1007/s42979-022-01215-9
remote_bool	true
author2	Falcao, Gabriel Paquete, Luís Fernandes, João Paulo
author2Str	Falcao, Gabriel Paquete, Luís Fernandes, João Paulo
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doi_str	10.1007/s42979-022-01215-9
up_date	2024-07-04T02:37:55.523Z
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