181 citations · 397 across the 13 of their papers we have counts for
13 papers
Observation of Improved Accuracy over Classical Sparse Ground-State Solvers using a Quantum Computer
William Kirby, Bibek Pokharel, Javier Robledo Moreno +25
Demonstrating quantum advantage over classical algorithms for ground state energy problems is an outstanding open problem in quantum computation. We experimentally demonstrate that…
Adiabatic state preparation from general initial states
Bryce Fuller, Mario Motta, Stuart M. Harwood +4
A variety of quantum computing algorithms exist for the preparation of approximate Hamiltonian ground states. A natural and important question is how these ground-state approximati…
Improved Quantum Computation using Operator Backpropagation
Bryce Fuller, Minh C. Tran, Danylo Lykov +10
Decoherence of quantum hardware is currently limiting its practical applications. At the same time, classical algorithms for simulating quantum circuits have progressed substantial…
Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization
Jeffery Yu, Javier Robledo Moreno, Joseph T. Iosue +17
Approximating the ground state of many-body systems is a key computational bottleneck underlying important applications in physics and chemistry. The most widely known quantum algo…
Tensor Network enhanced Dynamic Multiproduct Formulas
Niall F. Robertson, Bibek Pokharel, Bryce Fuller +10
Tensor networks and quantum computation are two of the most powerful tools for the simulation of quantum many-body systems. Rather than viewing them as competing approaches, here w…
Diagonalization of large many-body Hamiltonians on a quantum processor
Nobuyuki Yoshioka, Mirko Amico, William Kirby +16
The estimation of low energies of many-body systems is a cornerstone of computational quantum sciences. Variational quantum algorithms can be used to prepare ground states on pre-f…
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…
Challenges and Opportunities in Quantum Optimization
Amira Abbas, Andris Ambainis, Brandon Augustino +43
Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum a…
Potential and limitations of random Fourier features for dequantizing quantum machine learning
Ryan Sweke, Erik Recio-Armengol, Sofiene Jerbi +4
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning…
Quantum Kernel Alignment with Stochastic Gradient Descent
Gian Gentinetta, David Sutter, Christa Zoufal +2
Quantum support vector machines have the potential to achieve a quantum speedup for solving certain machine learning problems. The key challenge for doing so is finding good quantu…
Amplitude Estimation from Quantum Signal Processing
Patrick Rall, Bryce Fuller
Amplitude estimation algorithms are based on Grover's algorithm: alternating reflections about the input state and the desired outcome. But what if we are given the ability to perf…
Approximate Solutions of Combinatorial Problems via Quantum Relaxations
Bryce Fuller, Charles Hadfield, Jennifer R. Glick +8
Combinatorial problems are formulated to find optimal designs within a fixed set of constraints. They are commonly found across diverse engineering and scientific domains. Understa…
Second-quantized fermionic operators with polylogarithmic qubit and gate complexity
William Kirby, Bryce Fuller, Charles Hadfield +1
We present a method for encoding second-quantized fermionic systems in qubits when the number of fermions is conserved, as in the electronic structure problem. When the number …