From Ans\"atze to Z-gates: a NASA View of Quantum Computing

For the last few years, the NASA Quantum Artificial Intelligence Laboratory (QuAIL) has been performing research to assess the potential impact of quantum computers on challenging computational problems relevant to future NASA missions. A key aspect of this research is devising methods to most effectively utilize emerging quantum computing hardware. Research questions include what experiments on early quantum hardware would give the most insight into the potential impact of quantum computing, the design of algorithms to explore on such hardware, and the development of tools to minimize the quantum resource requirements. We survey work relevant to these questions, with a particular emphasis on our recent work in quantum algorithms and applications, in elucidating mechanisms of quantum mechanics and their uses for quantum computational purposes, and in simulation, compilation, and physics-inspired classical algorithms. To our early application thrusts in planning and scheduling, fault diagnosis, and machine learning, we add thrusts related to robustness of communication networks and the simulation of many-body systems for material science and chemistry. We provide a brief update on quantum annealing work, but concentrate on gate-model quantum computing research advances within the last couple of years.

[1]  Andrew J. Ochoa,et al.  Efficient Cluster Algorithm for Spin Glasses in Any Space Dimension. , 2015, Physical review letters.

[2]  Alexei Y. Kitaev,et al.  Quantum measurements and the Abelian Stabilizer Problem , 1995, Electron. Colloquium Comput. Complex..

[3]  Amir H. Khoshaman,et al.  GumBolt: Extending Gumbel trick to Boltzmann priors , 2018, NeurIPS.

[4]  Alexandru Paler,et al.  Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity , 2018, Physical Review X.

[5]  Herschel A Rabitz,et al.  Quantum Optimally Controlled Transition Landscapes , 2004, Science.

[6]  D. Venturelli,et al.  Quantum Annealing Implementation of Job-Shop Scheduling , 2015, 1506.08479.

[7]  Jérémie Roland,et al.  Anderson localization makes adiabatic quantum optimization fail , 2009, Proceedings of the National Academy of Sciences.

[8]  Daniel S. Levine,et al.  Postponing the orthogonality catastrophe: efficient state preparation for electronic structure simulations on quantum devices , 2018, 1809.05523.

[9]  M. Hastings,et al.  Gate count estimates for performing quantum chemistry on small quantum computers , 2013, 1312.1695.

[10]  Alán Aspuru-Guzik,et al.  Faster than classical quantum algorithm for dense formulas of exact satisfiability and occupation problems , 2015, New Journal of Physics.

[11]  Ronald de Wolf,et al.  Quantum Proofs for Classical Theorems , 2009, Theory Comput..

[12]  Stuart Hadfield,et al.  Quantum algorithms and circuits for scientific computing , 2015, Quantum Inf. Comput..

[13]  H. Neven,et al.  Low-Depth Quantum Simulation of Materials , 2018 .

[14]  Jonathan Carter,et al.  Computation of Molecular Spectra on a Quantum Processor with an Error-Resilient Algorithm , 2018 .

[15]  L. Lamata,et al.  From transistor to trapped-ion computers for quantum chemistry , 2013, Scientific Reports.

[16]  Eleanor G. Rieffel,et al.  Thermalization, Freeze-out, and Noise: Deciphering Experimental Quantum Annealers , 2017, 1703.03902.

[17]  Jeremy Frank,et al.  Parametrized Families of Hard Planning Problems from Phase Transitions , 2014, AAAI.

[18]  H. Nishimori,et al.  Quantum annealing in the transverse Ising model , 1998, cond-mat/9804280.

[19]  Ryan Babbush,et al.  Low rank representations for quantum simulation of electronic structure , 2018, npj Quantum Information.

[20]  Milad Marvian,et al.  Error suppression for Hamiltonian quantum computing in Markovian environments , 2016, 1612.01633.

[21]  Rupak Biswas,et al.  From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz , 2017, Algorithms.

[22]  M. Sipser,et al.  Quantum Computation by Adiabatic Evolution , 2000, quant-ph/0001106.

[23]  Stefan Edelkamp,et al.  Automated Planning: Theory and Practice , 2007, Künstliche Intell..

[24]  E. Farhi,et al.  A Quantum Approximate Optimization Algorithm , 2014, 1411.4028.

[25]  A. Scardicchio,et al.  Ergodic and localized regions in quantum spin glasses on the Bethe lattice , 2017, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[26]  A. Scardicchio,et al.  The many-body localized phase of the quantum random energy model , 2015, 1509.08926.

[27]  David Vázquez,et al.  PixelVAE: A Latent Variable Model for Natural Images , 2016, ICLR.

[28]  Kostyantyn Kechedzhi,et al.  Open system quantum annealing in mean field models with exponential degeneracy , 2015, 1505.05878.

[29]  Tad Hogg,et al.  Quantum-assisted associative adversarial network: applying quantum annealing in deep learning , 2019, Quantum Machine Intelligence.

[30]  Amir Khoshaman,et al.  A path towards quantum advantage in training deep generative models with quantum annealers , 2019, Mach. Learn. Sci. Technol..

[31]  J. Whitfield,et al.  Quantum Simulation of Helium Hydride Cation in a Solid-State Spin Register. , 2014, ACS nano.

[32]  Xi Chen,et al.  PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications , 2017, ICLR.

[33]  Stuart Hadfield,et al.  On the Representation of Boolean and Real Functions as Hamiltonians for Quantum Computing , 2018, ACM Transactions on Quantum Computing.

[34]  H. Neven,et al.  Characterizing quantum supremacy in near-term devices , 2016, Nature Physics.

[35]  Walter Vinci,et al.  Quantum variational autoencoder , 2018, Quantum Science and Technology.

[36]  Igor L. Markov,et al.  Simulating Quantum Computation by Contracting Tensor Networks , 2008, SIAM J. Comput..

[37]  Rupak Biswas,et al.  A flexible high-performance simulator for the verification and benchmarking of quantum circuits implemented on real hardware , 2018 .

[38]  Ewin Tang,et al.  A quantum-inspired classical algorithm for recommendation systems , 2018, Electron. Colloquium Comput. Complex..

[39]  Lov K. Grover A fast quantum mechanical algorithm for database search , 1996, STOC '96.

[40]  Rupak Biswas,et al.  Readiness of Quantum Optimization Machines for Industrial Applications , 2017, Physical Review Applied.

[41]  E. Knill,et al.  Quantum algorithms for fermionic simulations , 2000, cond-mat/0012334.

[42]  Alán Aspuru-Guzik,et al.  Faster quantum chemistry simulation on fault-tolerant quantum computers , 2012 .

[43]  H. Neven,et al.  Understanding Quantum Tunneling through Quantum Monte Carlo Simulations. , 2015, Physical review letters.

[44]  M. Head‐Gordon,et al.  Simulated Quantum Computation of Molecular Energies , 2005, Science.

[45]  J. Christopher Beck,et al.  A Hybrid Quantum-Classical Approach to Solving Scheduling Problems , 2016, SOCS.

[46]  Nicholas C. Rubin,et al.  $XY$-mixers: analytical and numerical results for QAOA , 2019, 1904.09314.

[47]  Guifré Vidal Efficient simulation of one-dimensional quantum many-body systems. , 2004, Physical review letters.

[48]  Vadim N. Smelyanskiy,et al.  On the relevance of avoided crossings away from quantum critical point to the complexity of quantum adiabatic algorithm , 2010, ArXiv.

[49]  Ewin Tang,et al.  Quantum-inspired classical algorithms for principal component analysis and supervised clustering , 2018, ArXiv.

[50]  Stuart Hadfield,et al.  The Quantum Approximation Optimization Algorithm for MaxCut: A Fermionic View , 2017, 1706.02998.

[51]  Travis S. Humble,et al.  Establishing the quantum supremacy frontier with a 281 Pflop/s simulation , 2019, Quantum Science and Technology.

[52]  John Preskill,et al.  Quantum Computing in the NISQ era and beyond , 2018, Quantum.

[53]  Hartmut Neven,et al.  Optimizing Variational Quantum Algorithms using Pontryagin's Minimum Principle , 2016, ArXiv.

[54]  J. McClean,et al.  Application of fermionic marginal constraints to hybrid quantum algorithms , 2018, 1801.03524.

[55]  Helmut G. Katzgraber,et al.  A deceptive step towards quantum speedup detection , 2017, Quantum Science and Technology.

[56]  Chris R. Jesshope,et al.  Parallel Computers 2: Architecture, Programming and Algorithms , 1981 .

[57]  Bryan O'Gorman,et al.  Parameterization of Tensor Network Contraction , 2019, TQC.

[58]  Mikhail Smelyanskiy,et al.  Practical optimization for hybrid quantum-classical algorithms , 2017, 1701.01450.

[59]  Richard M. Brown,et al.  The ILLIAC IV Computer , 1968, IEEE Transactions on Computers.

[60]  H. Rabitz,et al.  Role of controllability in optimizing quantum dynamics , 2009, 0910.4702.

[61]  A. Harrow,et al.  Quantum Supremacy through the Quantum Approximate Optimization Algorithm , 2016, 1602.07674.

[62]  David Gosset,et al.  Improved Classical Simulation of Quantum Circuits Dominated by Clifford Gates. , 2016, Physical review letters.

[63]  Walter Vinci,et al.  PixelVAE++: Improved PixelVAE with Discrete Prior , 2019, ArXiv.

[64]  Wenlong Wang,et al.  Patch-planting spin-glass solution for benchmarking. , 2017, Physical review. E.

[65]  Jeremy Frank,et al.  Compiling quantum circuits to realistic hardware architectures using temporal planners , 2017, ArXiv.

[66]  Rupak Biswas,et al.  Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models , 2016, 1609.02542.

[67]  Eyob A. Sete,et al.  A functional architecture for scalable quantum computing , 2016, 2016 IEEE International Conference on Rebooting Computing (ICRC).

[68]  S. Knysh,et al.  Quantum Optimization of Fully-Connected Spin Glasses , 2014, 1406.7553.

[69]  Z Papić,et al.  Local conservation laws and the structure of the many-body localized states. , 2013, Physical review letters.

[70]  Andrew J. Ochoa,et al.  Feeding the multitude: A polynomial-time algorithm to improve sampling. , 2018, Physical review. E.

[71]  E. Rieffel,et al.  Power of Pausing: Advancing Understanding of Thermalization in Experimental Quantum Annealers , 2018, Physical Review Applied.

[72]  John Preskill,et al.  Quantum computing and the entanglement frontier , 2012, 1203.5813.

[73]  Pieter Abbeel,et al.  Variational Lossy Autoencoder , 2016, ICLR.

[74]  Hartmut Neven,et al.  Path-integral quantum Monte Carlo simulation with open-boundary conditions , 2017 .

[75]  J. Imbrie Diagonalization and Many-Body Localization for a Disordered Quantum Spin Chain. , 2016, Physical review letters.

[76]  Ryan Babbush,et al.  Barren plateaus in quantum neural network training landscapes , 2018, Nature Communications.

[77]  R. Biswas,et al.  A quantum annealing approach for fault detection and diagnosis of graph-based systems , 2014, The European Physical Journal Special Topics.

[78]  Alán Aspuru-Guzik,et al.  A variational eigenvalue solver on a photonic quantum processor , 2013, Nature Communications.

[79]  S. Lloyd Quantum-Mechanical Computers , 1995 .

[80]  Alán Aspuru-Guzik,et al.  The theory of variational hybrid quantum-classical algorithms , 2015, 1509.04279.

[81]  N. Tubman,et al.  Molecular-Atomic Transition along the Deuterium Hugoniot Curve with Coupled Electron-Ion Monte Carlo Simulations. , 2014, Physical review letters.

[82]  H. Neven,et al.  Scaling analysis and instantons for thermally assisted tunneling and quantum Monte Carlo simulations , 2016, 1603.01293.

[83]  E. Rieffel,et al.  XY mixers: Analytical and numerical results for the quantum alternating operator ansatz , 2020 .

[84]  Thierry Paul,et al.  Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.

[85]  Jason Tyler Rolfe,et al.  Discrete Variational Autoencoders , 2016, ICLR.

[86]  Hartmut Neven,et al.  Nonergodic Delocalized States for Efficient Population Transfer within a Narrow Band of the Energy Landscape , 2018, Physical Review X.

[87]  Jarrod R. McClean,et al.  Computation of Molecular Spectra on a Quantum Processor with an Error-Resilient Algorithm , 2017, 1707.06408.

[88]  D. Huse,et al.  Phenomenology of fully many-body-localized systems , 2013, 1408.4297.

[89]  Peter W. Shor,et al.  Algorithms for quantum computation: discrete logarithms and factoring , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.

[90]  Stuart Hadfield,et al.  Divide and conquer approach to quantum Hamiltonian simulation , 2017 .

[91]  Helmut G. Katzgraber,et al.  The pitfalls of planar spin-glass benchmarks: raising the bar for quantum annealers (again) , 2017, 1703.00622.

[92]  B. Lanyon,et al.  Towards quantum chemistry on a quantum computer. , 2009, Nature chemistry.

[93]  E. Farhi,et al.  A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem , 2014, 1412.6062.

[94]  J. Carter,et al.  Hybrid Quantum-Classical Hierarchy for Mitigation of Decoherence and Determination of Excited States , 2016, 1603.05681.

[95]  N. Tubman,et al.  Renyi Entanglement Entropy of Molecules: Interaction Effects and Signatures of Bonding , 2012, 1204.4731.

[96]  Rupak Biswas,et al.  Quantum Annealing Applied to De-Conflicting Optimal Trajectories for Air Traffic Management , 2017, IEEE Transactions on Intelligent Transportation Systems.

[97]  Bryan O'Gorman,et al.  A case study in programming a quantum annealer for hard operational planning problems , 2014, Quantum Information Processing.

[98]  Martin Head-Gordon,et al.  A deterministic alternative to the full configuration interaction quantum Monte Carlo method. , 2016, The Journal of chemical physics.

[99]  M. Rigol,et al.  From quantum chaos and eigenstate thermalization to statistical mechanics and thermodynamics , 2015, 1509.06411.

[100]  Rupak Biswas,et al.  A NASA perspective on quantum computing: Opportunities and challenges , 2017, Parallel Comput..

[101]  D. Basko,et al.  Metal–insulator transition in a weakly interacting many-electron system with localized single-particle states , 2005, cond-mat/0506617.

[102]  Michael Broughton,et al.  A quantum algorithm to train neural networks using low-depth circuits , 2017, 1712.05304.

[103]  J. O'Brien,et al.  Witnessing eigenstates for quantum simulation of Hamiltonian spectra , 2016, Science Advances.

[104]  Ryan Babbush,et al.  What is the Computational Value of Finite Range Tunneling , 2015, 1512.02206.

[105]  Yuri I. Manin,et al.  Mathematics as Metaphor , 2007 .

[106]  Daniel A. Lidar,et al.  Evidence for quantum annealing with more than one hundred qubits , 2013, Nature Physics.

[107]  Bryan O'Gorman,et al.  An Investigation of Phase Transitions in Single-Machine Scheduling Problems , 2017, ICAPS.

[108]  Zhang Jiang,et al.  Non-commuting two-local Hamiltonians for quantum error suppression , 2015, Quantum Inf. Process..

[109]  A. Scardicchio,et al.  Many-body mobility edge in a mean-field quantum spin glass. , 2014, Physical review letters.

[110]  Ahmed Sameh,et al.  The Illiac IV system , 1972 .

[111]  Bryan O'Gorman,et al.  Quantum Circuit Compilation : An Emerging Application for Automated Reasoning , 2019 .

[112]  D. Abrams,et al.  Simulation of Many-Body Fermi Systems on a Universal Quantum Computer , 1997, quant-ph/9703054.

[113]  E. Rieffel,et al.  Near-optimal quantum circuit for Grover's unstructured search using a transverse field , 2017, 1702.02577.

[114]  S. Lloyd,et al.  Quantum Algorithm Providing Exponential Speed Increase for Finding Eigenvalues and Eigenvectors , 1998, quant-ph/9807070.

[115]  P. Coveney,et al.  Scalable Quantum Simulation of Molecular Energies , 2015, 1512.06860.

[116]  Alán Aspuru-Guzik,et al.  Quantum Simulation of Electronic Structure with Linear Depth and Connectivity. , 2017, Physical review letters.

[117]  H. Katzgraber,et al.  Exponentially Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians. , 2016, Physical review letters.

[118]  Manuela Herman,et al.  Quantum Computing: A Gentle Introduction , 2011 .

[119]  Cedric Yen-Yu Lin,et al.  Performance of QAOA on Typical Instances of Constraint Satisfaction Problems with Bounded Degree , 2016, ArXiv.

[120]  A. Scardicchio,et al.  Integrals of motion in the many-body localized phase , 2014, 1406.2175.

[121]  Rupak Biswas,et al.  Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers , 2017, Quantum Science and Technology.

[122]  H Neven,et al.  A blueprint for demonstrating quantum supremacy with superconducting qubits , 2017, Science.

[123]  J. Gambetta,et al.  Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets , 2017, Nature.

[124]  K. B. Whaley,et al.  Generalized Unitary Coupled Cluster Wave functions for Quantum Computation. , 2018, Journal of chemical theory and computation.

[125]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[126]  Alejandro Perdomo-Ortiz,et al.  Quantum-assisted Helmholtz machines: A quantum–classical deep learning framework for industrial datasets in near-term devices , 2017, ArXiv.

[127]  M. Troyer,et al.  Elucidating reaction mechanisms on quantum computers , 2016, Proceedings of the National Academy of Sciences.

[128]  Freeman J. Dyson,et al.  Mathematics as metaphor : selected essays of Yuri I. Manin , 2007 .

[129]  Daniel A. Lidar,et al.  Defining and detecting quantum speedup , 2014, Science.

[130]  Ying Wai Li,et al.  QMCPACK: an open source ab initio quantum Monte Carlo package for the electronic structure of atoms, molecules and solids , 2018, Journal of physics. Condensed matter : an Institute of Physics journal.

[131]  Seth Lloyd,et al.  Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension , 2018, ArXiv.

[132]  Koray Kavukcuoglu,et al.  Pixel Recurrent Neural Networks , 2016, ICML.

[133]  John Z. Imbrie,et al.  On Many-Body Localization for Quantum Spin Chains , 2014, 1403.7837.

[134]  Matthew B. Hastings,et al.  Towards Practical Quantum Variational Algorithms , 2015 .

[135]  Aram W. Harrow,et al.  Quantum computational supremacy , 2017, Nature.

[136]  Alejandro Perdomo-Ortiz,et al.  Strengths and weaknesses of weak-strong cluster problems: A detailed overview of state-of-the-art classical heuristics versus quantum approaches , 2016, 1604.01746.

[137]  B. Alder,et al.  Prospects for release-node quantum Monte Carlo. , 2011, The Journal of chemical physics.

[138]  H. Rabitz,et al.  Singularities of quantum control landscapes , 2009, 0907.2354.

[139]  Masoud Mohseni,et al.  Efficient Population Transfer via Non-Ergodic Extended States in Quantum Spin Glass , 2018, TQC.

[140]  M. Benedetti,et al.  Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning , 2015, 1510.07611.

[141]  Christof Zalka GROVER'S QUANTUM SEARCHING ALGORITHM IS OPTIMAL , 1997, quant-ph/9711070.

[142]  François Le Gall,et al.  Average-case quantum advantage with shallow circuits , 2018, CCC.

[143]  H. Rabitz,et al.  Quantum Control Landscapes Are Almost Always Trap Free , 2016, 1608.06198.

[144]  G. Kuperberg Random words, quantum statistics, central limits, Random matrices , 1999, math/9909104.

[145]  J. Christopher Beck,et al.  Comparing and Integrating Constraint Programming and Temporal Planning for Quantum Circuit Compilation , 2018, ICAPS.

[146]  Max Welling,et al.  Semi-supervised Learning with Deep Generative Models , 2014, NIPS.

[147]  M. Hastings,et al.  Training A Quantum Optimizer , 2016, 1605.05370.

[148]  Prasad Raghavendra,et al.  Beating the random assignment on constraint satisfaction problems of bounded degree , 2015, Electron. Colloquium Comput. Complex..

[149]  J. Christopher Beck,et al.  Explorations of Quantum-Classical Approaches to Scheduling a Mars Lander Activity Problem , 2016, AAAI Workshop: Planning for Hybrid Systems.

[150]  S. Knysh,et al.  Quantum Annealing via Environment-Mediated Quantum Diffusion. , 2015, Physical review letters.

[151]  J. Frank,et al.  Compiling Planning into Quantum Optimization Problems: A Comparative Study , 2015 .

[152]  C. Monthus Random transverse field spin-glass model on the Cayley tree: phase transition between the two many-body-localized phases , 2017, 1707.04039.