The efficient calculation of Hamiltonian spectra, a problem often intractable on classical machines, can find application in many fields, from physics to chemistry. We introduce the concept of an "eigenstate witness" and, through it, provide a new quantum approach that combines variational methods and phase estimation to approximate eigenvalues for both ground and excited states. This protocol is experimentally verified on a programmable silicon quantum photonic chip, a mass-manufacturable platform, which embeds entangled state generation, arbitrary controlled unitary operations, and projective measurements. Both ground and excited states are experimentally found with fidelities >99%, and their eigenvalues are estimated with 32 bits of p...
Under suitable assumptions, the algorithms in [Lin, Tong, Quantum 2020] can estimate the ground stat...
Quantum phase estimation (QPE) is the workhorse behind any quantum algorithm and a promising method...
A quantum algorithm solves computational tasks using fewer physical resources than the best-known cl...
The efficient calculation of Hamiltonian spectra, a problem often intractable on classical machines,...
Quantum computers promise to efficiently solve important problems that are intractable on a conventi...
In this work we present a detailed analysis of variational quantum phase estimation (VQPE), a method...
Despite the raw computational power of classical computers, some problems require an exponential amo...
We revisit quantum phase estimation algorithms for the purpose of obtaining the energy levels of man...
Variational algorithms for strongly correlated chemical and materials systems are one of the most pr...
We propose a quantum algorithm to obtain the lowest eigenstate of any Hamiltonian simulated by a qua...
Harnessing the full power of nascent quantum processors requires the efficient management of a limit...
We present a novel method for solving eigenvalue problems on a quantum computer based on spectroscop...
In this work we present a detailed analysis of variational quantum phase estimation (VQPE), a method...
Calculating the energy spectrum of a quantum system is an important task, for example to analyze rea...
Solving for molecular excited states remains one of the key challenges of modern quantum chemistry. ...
Under suitable assumptions, the algorithms in [Lin, Tong, Quantum 2020] can estimate the ground stat...
Quantum phase estimation (QPE) is the workhorse behind any quantum algorithm and a promising method...
A quantum algorithm solves computational tasks using fewer physical resources than the best-known cl...
The efficient calculation of Hamiltonian spectra, a problem often intractable on classical machines,...
Quantum computers promise to efficiently solve important problems that are intractable on a conventi...
In this work we present a detailed analysis of variational quantum phase estimation (VQPE), a method...
Despite the raw computational power of classical computers, some problems require an exponential amo...
We revisit quantum phase estimation algorithms for the purpose of obtaining the energy levels of man...
Variational algorithms for strongly correlated chemical and materials systems are one of the most pr...
We propose a quantum algorithm to obtain the lowest eigenstate of any Hamiltonian simulated by a qua...
Harnessing the full power of nascent quantum processors requires the efficient management of a limit...
We present a novel method for solving eigenvalue problems on a quantum computer based on spectroscop...
In this work we present a detailed analysis of variational quantum phase estimation (VQPE), a method...
Calculating the energy spectrum of a quantum system is an important task, for example to analyze rea...
Solving for molecular excited states remains one of the key challenges of modern quantum chemistry. ...
Under suitable assumptions, the algorithms in [Lin, Tong, Quantum 2020] can estimate the ground stat...
Quantum phase estimation (QPE) is the workhorse behind any quantum algorithm and a promising method...
A quantum algorithm solves computational tasks using fewer physical resources than the best-known cl...