add reference

docs/notebooks/cvar-cost-function.ipynb

@@ -16,10 +16,14 @@
     "This notebook shows how to use the Conditional Value at Risk (CVaR) objective function introduced in [1] within the variational quantum optimization algorithms. For a given set of shots with corresponding objective values of the considered optimization problem, the CVaR with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots.\n",
     "Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, but still applying some averaging to smoothen the optimization landscape.\n",
     "\n",
+    "The notebook also shows how twirling and CVaR can be used in conjunction to obtain error mitigated probability distributions as show in [2].\n",
+    "\n",
     "\n",
     "## References\n",
     "\n",
-    "[1] [P. Barkoutsos et al., *Improving Variational Quantum Optimization using CVaR,* Quantum 4, 256 (2020).](https://quantum-journal.org/papers/q-2020-04-20-256/)"
+    "[1] [P. Barkoutsos et al., *Improving Variational Quantum Optimization using CVaR,* Quantum 4, 256 (2020).](https://quantum-journal.org/papers/q-2020-04-20-256/)\n",
+    "\n",
+    "[2] [S. Barron et al., *Provable bounds for noise-free expectation values computed from noisy samples,* arXiv:231200733](https://arxiv.org/abs/2312.00733)"
    ]
   },
   {