We improve upon a recently introduced efficient quantum state reconstruction procedure targeted to states well approximated by the multiscale entanglement renormalization ansatz (MERA), e.g., ground states of critical models. We show how to numerically select a subset of experimentally accessible measurements which maximize information extraction about renormalized particles, thus dramatically reducing the required number of physical measurements. We numerically estimate the number of measurements required to characterize the ground state of the critical one-dimensional Ising (resp. XX) model and find that MERA tomography on 16-qubit (resp. 24-qubit) systems requires the same experimental effort as brute-force tomography on 8 qubits. We der...
Quantum state tomography (QST) is the gold standard technique for obtaining an estimate for the stat...
Recently, tremendous progress has been made in the field of quantum science and technologies: differ...
Recent research has demonstrated the usefulness of neural networks as variational ansatz functions f...
We improve upon a recently introduced efficient quantum state reconstruction procedure targeted to s...
Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimati...
We propose algorithms, based on the multi-scale entanglement renormalization ansatz, to obtain the ...
We propose algorithms, based on the multi-scale entanglement renormalization ansatz, to obtain the ...
Quantum state tomography-deducing quantum states from measured data-is the gold standard for verific...
Quantum computation - the use of quantum systems as bits, or qubits, to perform computation - has be...
Quantum computation - the use of quantum systems as bits, or qubits, to perform computation - has be...
While standard approaches to quantum simulation require a number of qubits proportional to the numbe...
Quantum state tomography is an essential tool for the characterization and verification of quantum s...
Quantum state tomography (QST) is the gold standard technique for obtaining an estimate for the stat...
Quantum state tomography (QST) is the gold standard technique for obtaining an estimate for the stat...
Recently, tremendous progress has been made in the field of quantum science and technologies: differ...
Quantum state tomography (QST) is the gold standard technique for obtaining an estimate for the stat...
Recently, tremendous progress has been made in the field of quantum science and technologies: differ...
Recent research has demonstrated the usefulness of neural networks as variational ansatz functions f...
We improve upon a recently introduced efficient quantum state reconstruction procedure targeted to s...
Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimati...
We propose algorithms, based on the multi-scale entanglement renormalization ansatz, to obtain the ...
We propose algorithms, based on the multi-scale entanglement renormalization ansatz, to obtain the ...
Quantum state tomography-deducing quantum states from measured data-is the gold standard for verific...
Quantum computation - the use of quantum systems as bits, or qubits, to perform computation - has be...
Quantum computation - the use of quantum systems as bits, or qubits, to perform computation - has be...
While standard approaches to quantum simulation require a number of qubits proportional to the numbe...
Quantum state tomography is an essential tool for the characterization and verification of quantum s...
Quantum state tomography (QST) is the gold standard technique for obtaining an estimate for the stat...
Quantum state tomography (QST) is the gold standard technique for obtaining an estimate for the stat...
Recently, tremendous progress has been made in the field of quantum science and technologies: differ...
Quantum state tomography (QST) is the gold standard technique for obtaining an estimate for the stat...
Recently, tremendous progress has been made in the field of quantum science and technologies: differ...
Recent research has demonstrated the usefulness of neural networks as variational ansatz functions f...