6 papers
Benchmarking Quantum and Classical Machine Learning Models on Oncological Data
Sydney Leither, Thomas Lubinski, Michael Kubal +1
Machine learning is being increasingly used for the detection, diagnosis, and treatment of cancer. However, models often struggle with biological data due to high dimensionality, l…
How many qubits does a machine learning problem require?
Sydney Leither, Michael Kubal, Sonika Johri
For a machine learning paradigm to be generally applicable, it should have the property of universal approximation, that is, it should be able to approximate any target function to…
Platform-Agnostic Modular Architecture for Quantum Benchmarking
Neer Patel, Anish Giri, Hrushikesh Pramod Patil +6
We present a platform-agnostic modular architecture that addresses the increasingly fragmented landscape of quantum computing benchmarking by decoupling problem generation, circuit…
A Quantum Platform for Multiomics Data
Michael Kubal, Sonika Johri
The complexity of biological systems, governed by molecular interactions across hierarchical scales, presents a challenge for computational modeling. While advances in multiomic pr…
A Practical Framework for Assessing the Performance of Observable Estimation in Quantum Simulation
Siyuan Niu, Efekan Kökcü, Sonika Johri +5
Simulating dynamics of physical systems is a key application of quantum computing, with potential impact in fields such as condensed matter physics and quantum chemistry. However,…
Bit-bit encoding, optimizer-free training and sub-net initialization: techniques for scalable quantum machine learning
Sonika Johri
Quantum machine learning for classical data is currently perceived to have a scalability problem due to (i) a bottleneck at the point of loading data into quantum states, (ii) the…