Application
AI and quantum machine learning techniques for quantum process identification
Quantum Information and Quantum Optics
Group Description:
The Quantum Information and Quantum Optics groups in the Physics department of the UAB have been working for years on topics related to the use of AI and machine learning for the optimization of quantum resources and to address some of the open questions in high-energy physics and condensed matter physics. On the group’s website, you can find more than 400 scientific publications in the field of quantum information, several of which focus on using AI and machine learning to understand quantum systems better. This includes methods for certifying and verifying quantum states. This experience is crucial for discerning whether what is generated on a quantum computer is indeed the state we want to study and not one that has been generated in error.
Activity description:
The goal of this task is to study the frontier between quantum algorithms and artificial intelligence and their application to the improvement of specifically quantum tasks. In particular, the following aspects will be addressed:
- Classification of quantum states from experimental data.
- Discrimination of quantum maps or processes using AI techniques.
- Certification and verification of quantum resources through quantum learning.
- Development of algorithms useful for high-energy physics and their evaluation on various quantum and classical platforms.
Results
Rout, S.; Sankar Bhattacharya, S.; Horodecki, P.
Randomness-free Detection of Non-projective Measurements: Qubits & beyond Unpublished
Preprint, 2024.
Abstract | Links | BibTeX | Tags: UAB
@unpublished{nokey,
title = {Randomness-free Detection of Non-projective Measurements: Qubits & beyond},
author = {Rout, S. and Sankar Bhattacharya, S. and Horodecki, P.},
url = {https://doi.org/10.48550/arXiv.2412.00213},
doi = {doi.org/10.48550/arXiv.2412.00213},
year = {2024},
date = {2024-11-29},
abstract = {Non-projective measurements are resourceful in several information-processing protocols. In this work, we propose an operational task involving space-like separated parties to detect measurements that are neither projective nor a classical post-processing of data obtained from a projective measurement. In the case of qubits, we consider a bipartite scenario and different sets of target correlations. While some correlations in each of these sets can be obtained by performing non-projective measurements on some shared two-qubit state it is impossible to simulate correlation in any of them using projective simulable measurements on bipartite qubit states or equivalently one bit of shared randomness. While considering certain sets of target correlations we show that the detection of qubit non-projective measurement is robust under arbitrary depolarising noise (except in the limiting case). For qutrits, while considering a similar task we show that some correlations obtained from local non-projective measurements are impossible to be obtained while performing the same qutrit projective simulable measurements by both parties. We provide numerical evidence of its robustness under arbitrary depolarising noise. For a more generic consideration (bipartite and tripartite scenario), we provide numerical evidence for a projective-simulable bound on the reward function for our task. We also show a violation of this bound by using qutrit POVMs. From a foundational perspective, we extend the notion of non-projective measurements to general probabilistic theories (GPTs) and use a randomness-free test to demonstrate that a class of GPTs, called square-bits or box-world are unphysical.},
howpublished = {Preprint},
keywords = {UAB},
pubstate = {published},
tppubtype = {unpublished}
}
Non-projective measurements are resourceful in several information-processing protocols. In this work, we propose an operational task involving space-like separated parties to detect measurements that are neither projective nor a classical post-processing of data obtained from a projective measurement. In the case of qubits, we consider a bipartite scenario and different sets of target correlations. While some correlations in each of these sets can be obtained by performing non-projective measurements on some shared two-qubit state it is impossible to simulate correlation in any of them using projective simulable measurements on bipartite qubit states or equivalently one bit of shared randomness. While considering certain sets of target correlations we show that the detection of qubit non-projective measurement is robust under arbitrary depolarising noise (except in the limiting case). For qutrits, while considering a similar task we show that some correlations obtained from local non-projective measurements are impossible to be obtained while performing the same qutrit projective simulable measurements by both parties. We provide numerical evidence of its robustness under arbitrary depolarising noise. For a more generic consideration (bipartite and tripartite scenario), we provide numerical evidence for a projective-simulable bound on the reward function for our task. We also show a violation of this bound by using qutrit POVMs. From a foundational perspective, we extend the notion of non-projective measurements to general probabilistic theories (GPTs) and use a randomness-free test to demonstrate that a class of GPTs, called square-bits or box-world are unphysical.
Rout, S.; N. Bhattacharya Sakharwade, S. S.; Ramanathan, R.; Horodecki, P.
Unbounded Quantum Advantage in Communication with Minimal Input Scaling Unpublished
Preprint, 2024.
Abstract | Links | BibTeX | Tags: UAB
@unpublished{nokey,
title = {Unbounded Quantum Advantage in Communication with Minimal Input Scaling},
author = {Rout,S. and Sakharwade, N. Bhattacharya, S.S. and Ramanathan, R. and Horodecki, P.},
url = {https://arxiv.org/pdf/2305.10372},
doi = {doi.org/10.48550/arXiv.2305.10372},
year = {2024},
date = {2024-11-29},
urldate = {2024-11-29},
abstract = {In communication complexity-like problems, previous studies have shown either an exponential quantum advantage or an unbounded quantum advantage with an exponentially large input set Θ(2n) bits with respect to classical communication Θ(n) bits. In the former, the quantum and classical separation grows exponentially in input while the latter's quantum communication resource is a constant. Remarkably, it was still open whether an unbounded quantum advantage exists while the inputs do not scale exponentially. Here we answer this question affirmatively using an input size of optimal order. Considering two variants as tasks: 1) distributed computation of relation and 2) {it relation reconstruction}, we study the one-way zero-error communication complexity of a relation induced by a distributed clique labelling problem for orthogonality graphs. While we prove no quantum advantage in the first task, we show an {it unbounded quantum advantage} in relation reconstruction without public coins. Specifically, for a class of graphs with order m, the quantum complexity is Θ(1) while the classical complexity is Θ(logm). Remarkably, the input size is Θ(logm) bits and the order of its scaling with respect to classical communication is {it minimal}. This is exponentially better compared to previous works. Additionally, we prove a lower bound (linear in the number of maximum cliques) on the amount of classical public coin necessary to overcome the separation in the scenario of restricted communication and connect this to the existence of Orthogonal Arrays. Finally, we highlight some applications of this task to semi-device-independent dimension witnessing as well as to the detection of Mutually Unbiased Bases.},
howpublished = {Preprint},
keywords = {UAB},
pubstate = {published},
tppubtype = {unpublished}
}
In communication complexity-like problems, previous studies have shown either an exponential quantum advantage or an unbounded quantum advantage with an exponentially large input set Θ(2n) bits with respect to classical communication Θ(n) bits. In the former, the quantum and classical separation grows exponentially in input while the latter's quantum communication resource is a constant. Remarkably, it was still open whether an unbounded quantum advantage exists while the inputs do not scale exponentially. Here we answer this question affirmatively using an input size of optimal order. Considering two variants as tasks: 1) distributed computation of relation and 2) {it relation reconstruction}, we study the one-way zero-error communication complexity of a relation induced by a distributed clique labelling problem for orthogonality graphs. While we prove no quantum advantage in the first task, we show an {it unbounded quantum advantage} in relation reconstruction without public coins. Specifically, for a class of graphs with order m, the quantum complexity is Θ(1) while the classical complexity is Θ(logm). Remarkably, the input size is Θ(logm) bits and the order of its scaling with respect to classical communication is {it minimal}. This is exponentially better compared to previous works. Additionally, we prove a lower bound (linear in the number of maximum cliques) on the amount of classical public coin necessary to overcome the separation in the scenario of restricted communication and connect this to the existence of Orthogonal Arrays. Finally, we highlight some applications of this task to semi-device-independent dimension witnessing as well as to the detection of Mutually Unbiased Bases.
Rout, S.; Sakharwade, N.; Bhattacharya, S. S.; Ramanathan, R.; Horodecki, P.
Unbounded Quantum Advantage in Communication with Minimal Input Scaling Unpublished
Preprint, 0000.
Abstract | Links | BibTeX | Tags: UAB
@unpublished{nokey,
title = {Unbounded Quantum Advantage in Communication with Minimal Input Scaling},
author = {Rout, S. and Sakharwade, N. and Bhattacharya, S.S. and Ramanathan, R. and Horodecki, P.},
url = {https://arxiv.org/pdf/2305.10372},
doi = {doi.org/10.48550/arXiv.2305.10372},
abstract = {In communication complexity-like problems, previous studies have shown either an exponential quantum advantage or an unbounded quantum advantage with an exponentially large input set Θ(2n) bits with respect to classical communication Θ(n) bits. In the former, the quantum and classical separation grows exponentially in input while the latter's quantum communication resource is a constant. Remarkably, it was still open whether an unbounded quantum advantage exists while the inputs do not scale exponentially. Here we answer this question affirmatively using an input size of optimal order. Considering two variants as tasks: 1) distributed computation of relation and 2) {it relation reconstruction}, we study the one-way zero-error communication complexity of a relation induced by a distributed clique labelling problem for orthogonality graphs. While we prove no quantum advantage in the first task, we show an {it unbounded quantum advantage} in relation reconstruction without public coins. Specifically, for a class of graphs with order m, the quantum complexity is Θ(1) while the classical complexity is Θ(logm). Remarkably, the input size is Θ(logm) bits and the order of its scaling with respect to classical communication is {it minimal}. This is exponentially better compared to previous works. Additionally, we prove a lower bound (linear in the number of maximum cliques) on the amount of classical public coin necessary to overcome the separation in the scenario of restricted communication and connect this to the existence of Orthogonal Arrays. Finally, we highlight some applications of this task to semi-device-independent dimension witnessing as well as to the detection of Mutually Unbiased Bases.},
howpublished = {Preprint},
keywords = {UAB},
pubstate = {published},
tppubtype = {unpublished}
}
In communication complexity-like problems, previous studies have shown either an exponential quantum advantage or an unbounded quantum advantage with an exponentially large input set Θ(2n) bits with respect to classical communication Θ(n) bits. In the former, the quantum and classical separation grows exponentially in input while the latter's quantum communication resource is a constant. Remarkably, it was still open whether an unbounded quantum advantage exists while the inputs do not scale exponentially. Here we answer this question affirmatively using an input size of optimal order. Considering two variants as tasks: 1) distributed computation of relation and 2) {it relation reconstruction}, we study the one-way zero-error communication complexity of a relation induced by a distributed clique labelling problem for orthogonality graphs. While we prove no quantum advantage in the first task, we show an {it unbounded quantum advantage} in relation reconstruction without public coins. Specifically, for a class of graphs with order m, the quantum complexity is Θ(1) while the classical complexity is Θ(logm). Remarkably, the input size is Θ(logm) bits and the order of its scaling with respect to classical communication is {it minimal}. This is exponentially better compared to previous works. Additionally, we prove a lower bound (linear in the number of maximum cliques) on the amount of classical public coin necessary to overcome the separation in the scenario of restricted communication and connect this to the existence of Orthogonal Arrays. Finally, we highlight some applications of this task to semi-device-independent dimension witnessing as well as to the detection of Mutually Unbiased Bases.
