ੴ ਸਤਿਗੁਰ ਪ੍ਰਸਾਦਿ ॥

PhD applicant for Fall 2027

Quantum states.
Chemical insight.

Quantum information × Molecular modeling

I study how to trust a quantum network's output, and how to learn chemical Hamiltonians while preserving their physical structure.

Explore my research Résumé & CV

At Carnegie Mellon. Let's talk about the work ↗

01 Entanglement & statistical guarantees 02 Learning chemical Hamiltonians

The research atom

Select a node ↗

My research, in orbit.

Learning and verification: the methods connecting my research Learning &verification CORE METHODS Quantum security, master’s thesis Quantumsecurity Quantum error correction, research interest Errorcorrection Entanglement certification, current research Entanglementcertification Quantum chemistry with Professor David Yaron, current research Quantumchemistry Quantum algorithms, circuits and optimization projects Quantumalgorithms

Choose an electron to explore the work around it.

The nucleus · connecting methods

Learning & verification

The thread through my work: learning useful models, preserving physical structure, and checking the evidence behind their predictions.

Explore my research ↗

About

I'm a graduate student at Carnegie Mellon University's Information Networking Institute, finishing an MS in Information Technology – Information Security in May 2027. My undergraduate degree is in Computer Engineering, where I graduated valedictorian with a 3.83/4.00 GPA.

My path into quantum computing came through networking and security. I began by studying reinforcement-learning policies for routing entanglement, then found that routing performance alone does not tell a user whether the pair they receive is good. That question now drives my work on certifying delivered entanglement: I want algorithms to be useful, but I also want to know what evidence supports their promises.

I'm applying to PhD programs for Fall 2027 to work on reliable quantum information systems. I am especially interested in quantum error correction and quantum algorithms for chemistry, building on my work in learning, networks, and statistical verification.

Tanish Singh Rajpal standing on a palm-lined campus walkway during a visit to Stanford University Campus visit
At Stanford University during a campus visit.

Research

Two guarantees for delivered entanglement A model-based guard supports conditional per-user protection. Hidden random tests of delivered pairs certify the kept-pair average, not any individual pair. Model guardconditional risk bound Delivered pairs Keep Hidden random tests Certify the kept-pair average DELIVERY + EVIDENCE
Model-based protection and measurement-based evidence.
01 / Quantum informationAuthor review

Certifying delivered entanglement

Independent Study, Information Networking Institute, CMU — with Prof. Michael Mattarock

I study what a quantum network can promise about the entanglement it delivers. A model-based guard supports per-user risk bounds; hidden random tests certify the average fidelity of kept pairs.

0.818IonQ median certificate vs. 0.817 predicted

Retrospective test splits; no certificate for an individual pair.

Updated November 2026

Read more: methods, evidence & limitations

What can a quantum network promise about the entangled pairs it delivers?

Per-pair promises need a model; hidden random tests can certify an average.

I separate two guarantees for delivered entanglement: a model-based guard bounds a user's risk of ever receiving a below-threshold pair under adaptive routing, while hidden random tests certify the average fidelity of the pairs kept. The guard needs a model and declared margins that cover the actual noise. The certificate needs trustworthy measurements and a fresh, hidden test choice; it does not certify any individual kept pair.

7 + 2
experimental-data re-analyses and local runs on one IBM processor
0.818
IonQ median certificate, compared with 0.817 predicted before reading the data
0 / 8.8M
bad guarded deliveries in topology simulations; descriptive count

The seven re-analyses cover ICFO, USTC, Innsbruck, Delft, Oxford, Harvard, and IonQ. On IonQ's ion–silicon-vacancy link, a 3,000-pair record—about three seconds at 1.03 kHz—gave the predicted certificate. The test-or-keep splits were drawn retrospectively and the measurement settings were collected in blocks, so this did not test an online, time-ordered protocol. On Oxford's randomised test-or-keep record, the trusted-settings certificate ended at 0.917 and stayed above 0.9 after about 220,000 kept pairs; the separate device-independent bound was 0.837.

The topology simulations cover cubes up to 1,000 nodes, square grids up to 37 × 37, and a grid with diagonal links. No guarded delivery was bad across 36 conditions with 32 simulated networks each; networks, rather than individual deliveries, are the independent replication units. These are simulations with simplified physics, not physical network trials. The two IBM runs tested local components on one processor. An alarm cannot undo earlier deliveries, and protection against arbitrary model drift is not established.

The manuscript is complete and under review by the authors before submission.

Read the updated research note ↗ Code, pre-registrations, and evidence ↗ Archived code releases ↗
Learning chemical Hamiltonians with physical structure A schematic Hamiltonian matrix and the expansion from 75 to 266 learned parameters under symmetry constraints. This is a diagram of the method, not a computed molecular result. PHYSICS-GUIDED LEARNING Hθ 75 → 266 parameters basis expansion · symmetry constraints
A larger basis, with physical structure built into the model.
02 / Quantum chemistryIn progress

Learning chemical Hamiltonians

Quantum chemistry independent study — Advisor: Prof. David Yaron, CMU Chemistry

I am extending DFTBML to a split-valence basis while preserving its physical structure. My work develops trainable Hamiltonian components and tests the symmetry constraints that tie them together.

75 → 266Learned parameters under symmetry constraints

Independent study with Prof. David Yaron, CMU Chemistry.

September 2026–present

Read more: model development & validation

I am extending DFTBML, a machine-learned density-functional tight-binding model for chemical Hamiltonians, to a split-valence (double-zeta) basis. The goal is to improve accuracy and transferability while preserving the model's physical structure.

  • Designed a four-shell basis in PyTorch, expanding the learned parameters from 75 to 266 under symmetry constraints.
  • Built a randomized-perturbation test suite to detect improperly tied parameters, and validated it with injected faults.
  • Calibrated PySCF integrals against reference Slater–Koster tables to initialize the new basis functions.
DFTBML background: Hu, He, and Yaron, JCTC (2023) ↗
Proposed hybrid post-quantum authentication architecture A wireless client connects through an edge QUIC proxy toward authentication services. The proposed wired backbone is secured with QKD. Evaluation in NS-3 is in progress. PROPOSED ARCHITECTURE Wirelessclient Edge proxyQUIC termination AuthenticationRADIUS QKD-secured backbone NS-3 · evaluation in progress
Testing how large post-quantum credentials affect authentication.
03 / Master’s thesisIn progress

Post-quantum network authentication

Master's Thesis, Information Networking Institute, CMU — Advisors: Prof. Pedro Bustamante & Prof. Michael Mattarock · Reader: Prof. Patrick Tague

I am exploring a hybrid architecture for large post-quantum authentication certificates: an edge QUIC proxy and a QKD-secured wired backbone. The work is being evaluated in NS-3.

NS-3Architecture evaluation in progress

Thesis defense and final report targeted for Spring 2027.

August 2026–April 2027

Read more: architecture & evaluation plan

Post-quantum cryptographic certificates are large enough to break assumptions that classical network protocols quietly depend on. This thesis looks at one specific failure mode: oversized ML-DSA (post-quantum) certificates used in EAP-TLS authentication can trigger silent Path MTU Discovery blackholes — connections that fail without any clear error, because a packet is dropped somewhere and nothing reports it.

The proposed fix combines an edge QUIC-terminating proxy at the Wireless LAN Controller with a Quantum Key Distribution–secured wired backbone, inside a hybrid WPA-Enterprise 802.1X architecture. The thesis is organized around four testable hypotheses, simulated in NS-3:

  • Handshake latency — targeting a ≥40% reduction in Flow Completion Time under elevated wireless error rates.
  • RADIUS server compute offload under the proxy architecture.
  • IPv4/IPv6 baseline isolation, to separate protocol-specific effects from the core result.
  • Sustained QKD key-delivery capacity under high-density authentication bursts.

Thesis defense and final report targeted for Spring 2027. Simulation code and datasets will be released via a CMU-hosted GitHub repository and the CMU KiltHub data repository.

Publications

Manuscript in author review

Rajpal, T. S., & Mattarock, M. (2026). What can a quantum network promise about the entangled pairs it delivers? Manuscript complete; author review before submission.

Conditional guarantees and statistical evidence for delivered entanglement, supported by simulations and experimental-data re-analyses.

Read study summary

Develops conditional per-user guarantees for entanglement delivery, an anytime certificate for kept-pair average fidelity, and a test-driven check of model optimism. Evidence includes network simulations, seven experimental-data re-analyses, and two local runs on one IBM processor. The IonQ re-analysis compared a certificate predicted before reading the data (0.817) with the observed median (0.818).

Code, results, and audit trail ↗

Journal articles

Rajpal, T. S., & Naithani, A. (2026). NeuroCrypt: A Neuro-Symbolic AI Ecosystem for Advanced Cryptographic Data Security and Transmission. IEEE Transactions on Artificial Intelligence, 7(1), 512–521.

A neuro-symbolic approach to symmetric-key generation, evaluated against classical ciphers and neural-network-based attacks.

Read study summary

NeuroCrypt pairs a rule-based cryptographic engine with a deep neural network to dynamically generate symmetric keys, aiming to make encryption harder to model and attack than a fixed algorithm. Evaluated against classical ciphers (AES, DES) and neural-network-based attacks, it showed a 25% increase in measured security resilience and a 15% reduction in key-management overhead.

doi.org/10.1109/TAI.2025.3577605

Conference papers

Naithani, A., & Rajpal, T. S. (2025). Blockchain-Based Digital Health Passport: A Decentralized Framework for Secure and Interoperable Health Data Management. 2025 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC).

A decentralized health-record framework combining patient-controlled consent, encryption, and off-chain storage.

Read study summary

A decentralized health-record system built to guarantee data immutability and patient ownership through a unique identifier and OTP-based consent mechanism, designed for secure interoperability across providers at national scale. It uses a hybrid on/off-chain storage model with public-key encryption and role-based access control, aiming to keep large medical records both secure and compliant without sacrificing scalability.

doi.org/10.1109/ZINC65316.2025.11100281

Naithani, A., Rajpal, T. S., & Mistry, V. (2024). TAV-Cart: Transaction Assistance and Validation Cart System for Effortless Shopping and Checkout. 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM).

A smart-cart architecture that pairs barcode scanning with weight validation and direct digital checkout.

Read study summary

A system design for a smart shopping cart intended as a lower-cost alternative to RFID-based carts: dual barcode scanners and a strain-gauge weight-validation system cross-check that what's scanned matches what's physically in the cart, an LED strip gives real-time feedback, and checkout happens directly through UPI — India's national instant-payment system — removing the need for a cashier line. This is a feasibility and system-architecture study, not a deployed retail pilot.

doi.org/10.1109/ICACCM61117.2024.11059075

Naithani, A., Jain, V., Rajpal, T. S., & Mistry, V. (2024). AI Based Goalkeeper for Penalty Shot Prediction Using RNN and Pattern Matching Algorithm. 2024 12th International Symposium on Digital Forensics and Security (ISDFS).

A recurrent neural network and pattern-matching prototype for predicting penalty-shot direction.

Read study summary

Built a predictive model — a recurrent neural network combined with a pattern-matching module — that learns a penalty taker's shot-direction tendencies from their history and predicts where they'll shoot next. Tested against four shooting strategies (a fixed repeating pattern, always the same side, fully random, and adaptively shooting away from the keeper's last dive) at 30 shots each: fully random shooting produced the lowest save rate of any strategy, directly showing that unpredictability is a penalty taker's strongest defense against a model trying to read them. Validated against Cristiano Ronaldo's recorded penalty history from the 2014–15 through 2017–18 seasons: the AI model saved 17 of 53 penalties in situations where the real goalkeepers on record saved 13 — a modest but real improvement, and an initial prototype rather than a production system.

doi.org/10.1109/ISDFS60797.2024.10527307 Code repository

Selected projects

The Last Call — a quantum interference horror game

Team Popeyes · Qollab × IonQ Global Quantum Hackathon · Global Best of Quantum Playground
The Last Call's two-door intercom, phase dial, and evidence chart
An AR horror game where interference and which-path information determine the choices you can make.

I teamed up with Abhinaya Nagalla to build a quantum horror game around one call arriving at two doors. Players listen for interference to identify the caller, trace the path to locate the door, and use a quantum eraser to recover patterns in sorted outcomes. The game runs on a regular screen or in AR; its Qiskit circuits and documented IonQ simulator run are available with the source.

The Last Call received the global Best of Quantum Playground specialty award. At the CMU node, it placed first among 12 projects and won Most Novel/Creative and Best Documentation.

Play The Last Call ↗ Read the project story ↗ Official CMU results ↗ Global specialty awards ↗ Source and validation ↗
October 9–11, 2026

Dicke-state circuits for Willow · Ask the Circuit

Team Popeyes · DuQuantum, Duke University · Google Quantum AI Challenge, Standard Track: third place · MLH Best Use of Gemini

I rebuilt a textbook Dicke-state construction for Willow's connectivity and wrote calibration-aware qubit-selection code. I also tested and merged four teammates' branches into one notebook, presented our technical design, and built the project website. Team Popeyes earned a Google Quantum AI Challenge score of 1.2766 ± 0.0127 and won MLH Best Use of Gemini for Ask the Circuit. The organizers' hardware rescoring challenged our simulation rankings and led us to revise the design.

Read the hackathon story ↗ Explore the project ↗ Code and technical write-up ↗
October 24–25, 2026

Cyber SimLab — human-in-the-loop AI for security operations

Carnegie Mellon University · Information Networking Institute practicum

On the practicum team, I am helping design an AI-assisted security operations training environment. The project explores how agents can support alert triage and incident investigation while keeping consequential response actions subject to human approval. We are also designing decision and tool-call logs so learners can examine how a recommendation was reached. The project is in progress.

August 2026–present

Phase Hunter — mapping quantum spin phases under noise

Team Lead · Team Popeyes · Q-SITE Hacks 2026 · Quantum Coalition Scientific Track runner-up
Phase Hunter browser game showing measurement points on a hidden quantum spin phase map and a player-drawn boundary
A playable measurement game backed by reproducible PennyLane simulations.

I led Team Popeyes and was the primary developer of Phase Hunter, working with Paul Jeon and Sumedhaa Ruhil. Players use a limited number of measurements to locate a hidden phase boundary in a ring of quantum spins. The accompanying notebook compares clean, noisy, and error-mitigated phase maps. In our simulated setting, the stripe phase lost more correlation signal while the aligned phase lost more classified map area. At the smallest shot budget tested, a lower-noise baseline outperformed error extrapolation.

Code and notebook ↗ Watch demo ↗
September 2026

Quantum portfolio optimization using QAOA — Team Lead

Yale University's YQuantum 2026 Hackathon — 3rd place
Markowitz portfolio model
QUBO formulation
Ising Hamiltonian
QAOA circuit

Built a quantum computing approach to optimizing insurance investment portfolios: mapping classical Markowitz portfolio theory into a QUBO formulation, then converting that into an Ising Hamiltonian solvable with the Quantum Approximate Optimization Algorithm. Led the team's circuit construction and problem formulation work over the hackathon.

View repository
April 2026

Experience

SEO & AI Intern

MotionGility · Indore, Madhya Pradesh, India

Supported content strategy through AI-assisted keyword research, search-trend analysis, and content-gap reviews. Worked on content optimization and automation to improve search visibility.

February–May 2025

Vulnerability Assessment and Penetration Testing Intern

Newton's Apple, Pune, India

Ran penetration testing on a confidential oil company's Android application using Burp Suite and Metasploit, identifying and helping remediate three critical vulnerabilities that protected sensitive operational data. Static Java code analysis work, done in collaboration with the development team, contributed to a 40% reduction in common security flaws in subsequent releases.

October 2023–January 2024

Recognition and leadership

Global Best of Quantum Playground · Qollab × IonQ

The Last Call · Team Popeyes · With Abhinaya Nagalla · Carnegie Mellon University node

Global Quantum Playground specialty-award winner. At the CMU node: first place among 12 projects, Most Novel/Creative, and Best Documentation.

Read the results and explore the project ↗ View the global award certificate ↗ Verify on Qollab ↗
October 2026

Two awards · DuQuantum 2026

Team Popeyes · Duke University

Third place in the Google Quantum AI Challenge, Standard Track (team score: 1.2766 ± 0.0127), and MLH Best Use of Gemini for Ask the Circuit.

View the certificate and award photo ↗
October 2026

Three podium finishes · Q-SITE Hacks 2026

Team Popeyes · University of Toronto · Qiskit Fall Fest 2026

Runner-up in the Quantum Coalition Open Challenge, Scientific Track; third place in the IBM Quantum Coding Challenge; and third place in the Overall Coding Challenge. Also placed fourth in the IonQ Championship, 0.44 points from third.

View certificates and results ↗
September 2026
IBM Qiskit Global Summer School 2026 Quantum Excellence badge

IBM Quantum · Advanced credential

Qiskit Global Summer School 2026 — Quantum Excellence

Earned IBM Quantum's advanced summer-school credential through core coursework and graded labs spanning hardware-aware circuits, noise and benchmarking, error mitigation, and paths toward quantum advantage—including QAOA and molecular quantum chemistry.

Verify credential ↗ View certificate
Issued August 21, 2026

Student Ambassador

Information Networking Institute, Carnegie Mellon University
October 2025–present

Third place · Yale YQuantum Hackathon

Quantum portfolio optimization project
2026

First place · Crypto Quest Cybersecurity Competition

Tech-Fest Ambiora
2024

For recruiters

Download my résumé for an overview of my work, or my academic CV for the full research and publication record.

PDF downloads · Updated November 2, 2026 · Get in touch ↗

Contact

Let's talk about quantum information systems.

I am applying to PhD programs for Fall 2027 and welcome conversations about reliable quantum networks, quantum error correction, and quantum algorithms for chemistry.

Open reach-out page Email directly

GitHub LinkedIn CV Résumé